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10.1002/bimj.202100125 http://scihub22266oqcxt.onion/10.1002/bimj.202100125 34378235!8426968!34378235     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biom+J 2021 ; 63 (8): 1623-1632 Nephropedia Template TP {{tp|p=34378235|t=2021. A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020 |pdf=|usr=}}{{34378235}} gab.com Text A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020 JO: Biom J http://www.kidney.de/pget.php?&tw=1&pmid=34378235 #FOAvip The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020. Twit Text FOAVip A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020 ????Biom J http://www.kidney.de/pget.php?&tw=1&pmid=34378235 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34378235 #FOAvip English Wikipedia <ref>{{Cite pmid|34378235}}</ref> A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020 #MMPMID34378235 Schneble M; De Nicola G; Kauermann G; Berger U Biom J 2021[Dec]; 63 (8): 1623-1632 PMID34378235show ga The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020. |*COVID-19[MESH] |*Pandemics[MESH] |Humans[MESH] |Models, Statistical[MESH]A statistical model for the dynamics of COVID-19 infections and their (epmc).pdf DeepDyve Pubget Overpricing